##        IDjoueur  nom_du_joueur heure_connexion_joueur nom_du_jeu
##    1: 02fmwoq6z    Lina Debien   10_25_2016_17h33m45s   Logique2
##    2: 02fmwoq6z    Lina Debien   10_25_2016_17h33m45s   Logique2
##    3: 02fmwoq6z    Lina Debien   10_25_2016_17h33m45s   Logique2
##    4: 02fmwoq6z    Lina Debien   10_25_2016_17h33m45s   Logique2
##    5: 02fmwoq6z    Lina Debien   10_25_2016_17h33m45s   Logique2
##   ---                                                           
## 6986: ysuq0jc98 Martin Lanchon   10_26_2016_16h00m21s    Motrice
## 6987: ysuq0jc98 Martin Lanchon   10_26_2016_16h00m21s    Motrice
## 6988: ysuq0jc98 Martin Lanchon   10_26_2016_16h00m21s    Motrice
## 6989: ysuq0jc98 Martin Lanchon   10_26_2016_16h00m21s    Motrice
## 6990: ysuq0jc98 Martin Lanchon   10_26_2016_16h00m21s    Motrice
##       action_de_jeu mise difficulty moutons_sauves moutons_tues score
##    1:             1    2        0.0              0            2    -2
##    2:             2    5        0.0              5            2     3
##    3:             3    1        0.1              5            3     2
##    4:             4    7        0.0             12            3     9
##    5:             5    7        0.1             12           10     2
##   ---                                                                
## 6986:            26    3        0.5             69           35    34
## 6987:            27    1        0.6             69           36    33
## 6988:            28    7        0.5             76           36    40
## 6989:            29    1        0.6             76           37    39
## 6990:            30    7        0.5             83           37    46
##       gagnant          horodateur      prenomNom age sexe langueMaternelle
##    1:       0 10/25/2016 17:41:48    Lina Debien  14    1                1
##    2:       1 10/25/2016 17:41:48    Lina Debien  14    1                1
##    3:       0 10/25/2016 17:41:48    Lina Debien  14    1                1
##    4:       1 10/25/2016 17:41:48    Lina Debien  14    1                1
##    5:       0 10/25/2016 17:41:48    Lina Debien  14    1                1
##   ---                                                                     
## 6986:       1 10/26/2016 16:06:56 Martin Lanchon  17    0                1
## 6987:       0 10/26/2016 16:06:56 Martin Lanchon  17    0                1
## 6988:       1 10/26/2016 16:06:56 Martin Lanchon  17    0                1
## 6989:       0 10/26/2016 16:06:56 Martin Lanchon  17    0                1
## 6990:       1 10/26/2016 16:06:56 Martin Lanchon  17    0                1
##       niveauEtude profilJoueur8
##    1:           1             0
##    2:           1             0
##    3:           1             0
##    4:           1             0
##    5:           1             0
##   ---                          
## 6986:           1             1
## 6987:           1             1
## 6988:           1             1
## 6989:           1             1
## 6990:           1             1
##                                                                                                   jeuxFav
##    1:                                         géometrie dash /color switch/fruit ninja      sur téléphone
##    2:                                         géometrie dash /color switch/fruit ninja      sur téléphone
##    3:                                         géometrie dash /color switch/fruit ninja      sur téléphone
##    4:                                         géometrie dash /color switch/fruit ninja      sur téléphone
##    5:                                         géometrie dash /color switch/fruit ninja      sur téléphone
##   ---                                                                                                    
## 6986: Jeu vidéo : League of Legend\nJeu vidéo :  Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6987: Jeu vidéo : League of Legend\nJeu vidéo :  Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6988: Jeu vidéo : League of Legend\nJeu vidéo :  Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6989: Jeu vidéo : League of Legend\nJeu vidéo :  Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
## 6990: Jeu vidéo : League of Legend\nJeu vidéo :  Pokémon\nJeu vidéo : Sylvette Rochette ( Fais soi-même )
##       autoEffJoueur1 autoEffJoueur2 autoEffJoueur3 autoEffJoueur4
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 6986:              5              4              5              5
## 6987:              5              4              5              5
## 6988:              5              4              5              5
## 6989:              5              4              5              5
## 6990:              5              4              5              5
##       autoEffJoueur5 autoEffJoueur6 autoEffJoueur7 autoEffJoueur8
##    1:             NA             NA             NA             NA
##    2:             NA             NA             NA             NA
##    3:             NA             NA             NA             NA
##    4:             NA             NA             NA             NA
##    5:             NA             NA             NA             NA
##   ---                                                            
## 6986:              5              5              5              5
## 6987:              5              5              5              5
## 6988:              5              5              5              5
## 6989:              5              5              5              5
## 6990:              5              5              5              5
##       autoEffJoueur9 autoEffJoueur10 loterie1 loterie2 loterie3 loterie4
##    1:             NA              NA        1        1        1        1
##    2:             NA              NA        1        1        1        1
##    3:             NA              NA        1        1        1        1
##    4:             NA              NA        1        1        1        1
##    5:             NA              NA        1        1        1        1
##   ---                                                                   
## 6986:              4               4        1        1        1        1
## 6987:              4               4        1        1        1        1
## 6988:              4               4        1        1        1        1
## 6989:              4               4        1        1        1        1
## 6990:              4               4        1        1        1        1
##       loterie5 loterie6 loterie7 loterie8 loterie9 loterie10
##    1:        0        0        0        0        0         0
##    2:        0        0        0        0        0         0
##    3:        0        0        0        0        0         0
##    4:        0        0        0        0        0         0
##    5:        0        0        0        0        0         0
##   ---                                                       
## 6986:        1        1        1        1        1         1
## 6987:        1        1        1        1        1         1
## 6988:        1        1        1        1        1         1
## 6989:        1        1        1        1        1         1
## 6990:        1        1        1        1        1         1
##       play.video.games play.board.games play.money.games self.eff
##    1:                1              0.4        0.3333333       NA
##    2:                1              0.4        0.3333333       NA
##    3:                1              0.4        0.3333333       NA
##    4:                1              0.4        0.3333333       NA
##    5:                1              0.4        0.3333333       NA
##   ---                                                            
## 6986:                1              0.6        0.3333333      4.7
## 6987:                1              0.6        0.3333333      4.7
## 6988:                1              0.6        0.3333333      4.7
## 6989:                1              0.6        0.3333333      4.7
## 6990:                1              0.6        0.3333333      4.7
## Nombre de participants se déclarant comme joueurs :  42
## Nombre de femmes se déclarant comme joueuses :  2

Removing Outliers

## [1] "Outliers : 135499aaw, 9l7s14ocz, 9l7s14ocz, g6m2iu73e, lpc2zjkex, srn0c21wi"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : at13n1mb2, srn0c21wi"
## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## Empty data.table (0 rows) of 1 col: IDjoueur

## [1] "Outliers : 2lvqyyzt9, 3t1l09dyk, e0tdz7cvh"

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur

## Empty data.table (0 rows) of 1 col: IDjoueur
## [1] "Total number of outliers:  9"
## [1] "Total number of outliers motor task:  1"
## [1] "Total number of outliers perceptive task:  3"
## [1] "Total number of outliers logical task:  6"

Modeling objective difficulty for motor task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   3151.9   3174.9  -1571.9   3143.9     2336 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.9957 -0.8530 -0.5675  0.9526  2.1623 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.6875   0.8292  
## Number of obs: 2340, groups:  IDjoueur, 78
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.1106     0.1603  -6.928 4.27e-12 ***
## difficulty    3.2297     0.3438   9.395  < 2e-16 ***
## timeNorm     -0.4618     0.1650  -2.798  0.00514 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.585       
## timeNorm   -0.216 -0.401
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0      2340         0 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-2.126430  
##  1st Qu.:-0.347466  
##  Median : 0.049837  
##  Mean   : 0.003376  
##  3rd Qu.: 0.448296  
##  Max.   : 1.960052  
## [1] "Intercept: -1.11 4.3e-12 ***"
## [1] "Difficulty: 3.23 5.7e-21 ***"
## [1] "Time: -0.462 0.0051 **"
## [1] "R2 fixed: 0.13"
## [1] "R2 mixed: 0.28"
## [1] "Cross Val: 0.61"
## [1] "AIC: 3200"
##          0%         25%         50%         75%        100% 
## -1.96005180 -0.44829605 -0.04983725  0.34746630  2.12642981

##          0%         25%         50%         75%        100% 
## -1.96005180 -0.44829605 -0.04983725  0.34746630  2.12642981

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for sensory task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2532.7   2555.3  -1262.3   2524.7     2126 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -4.5031 -0.7242 -0.3428  0.8058  3.7576 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 0.5808   0.7621  
## Number of obs: 2130, groups:  IDjoueur, 71
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -2.1935     0.1751 -12.529   <2e-16 ***
## difficulty    9.0967     0.5527  16.459   <2e-16 ***
## timeNorm     -0.3650     0.1824  -2.001   0.0454 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.615       
## timeNorm   -0.317 -0.299
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##         0         0      2130 
## [1] "Player levels from ranef:"
##   (Intercept)       
##  Min.   :-1.476587  
##  1st Qu.:-0.370451  
##  Median :-0.036883  
##  Mean   : 0.003092  
##  3rd Qu.: 0.449790  
##  Max.   : 1.689450  
## [1] "Intercept: -2.19 5.2e-36 ***"
## [1] "Difficulty: 9.1 7.2e-61 ***"
## [1] "Time: -0.365 0.045 *"
## [1] "R2 fixed: 0.31"
## [1] "R2 mixed: 0.42"
## [1] "Cross Val: 0.68"
## [1] "AIC: 2500"
##          0%         25%         50%         75%        100% 
## -1.68944968 -0.44979001  0.03688316  0.37045061  1.47658650

##          0%         25%         50%         75%        100% 
## -1.68944968 -0.44979001  0.03688316  0.37045061  1.47658650

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

Modeling objective difficulty for logical task

## Generalized linear mixed model fit by maximum likelihood (Laplace
##   Approximation) [glmerMod]
##  Family: binomial  ( logit )
## Formula: perdant ~ difficulty + timeNorm + (1 | IDjoueur)
##    Data: DT
## 
##      AIC      BIC   logLik deviance df.resid 
##   2839.0   2861.8  -1415.5   2831.0     2216 
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -2.7599 -0.7933 -0.4388  0.8894  5.9937 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  IDjoueur (Intercept) 1.538    1.24    
## Number of obs: 2220, groups:  IDjoueur, 74
## 
## Fixed effects:
##             Estimate Std. Error z value Pr(>|z|)    
## (Intercept)  -1.6820     0.1962  -8.574  < 2e-16 ***
## difficulty    4.8761     0.3902  12.497  < 2e-16 ***
## timeNorm     -1.0018     0.2129  -4.707 2.52e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##            (Intr) dffclt
## difficulty -0.435       
## timeNorm   -0.095 -0.617
## The result is correct only if all data used by the model has not changed since model was fitted.
## The result is correct only if all data used by the model has not changed since model was fitted.
## 
##  Logique2   Motrice Sensoriel 
##      2220         0         0 
## [1] "Player levels from ranef:"
##   (Intercept)      
##  Min.   :-2.40565  
##  1st Qu.:-0.91465  
##  Median :-0.21046  
##  Mean   : 0.00762  
##  3rd Qu.: 1.01654  
##  Max.   : 2.33338  
## [1] "Intercept: -1.68 1e-17 ***"
## [1] "Difficulty: 4.88 7.8e-36 ***"
## [1] "Time: -1 2.5e-06 ***"
## [1] "R2 fixed: 0.25"
## [1] "R2 mixed: 0.49"
## [1] "Cross Val: 0.65"
## [1] "AIC: 2800"
##         0%        25%        50%        75%       100% 
## -2.3333793 -1.0165435  0.2104629  0.9146509  2.4056548

##         0%        25%        50%        75%       100% 
## -2.3333793 -1.0165435  0.2104629  0.9146509  2.4056548

## `geom_smooth()` using method = 'gam'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

## `geom_smooth()` using method = 'loess'

Influence of Player Profiles

Player profiles

Influence of Player Profiles

Objective level and player profile

Playing video games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3248, p-value = 0.1852
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##     tau 
## 0.11704

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.27298, p-value = 0.7849
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02518738

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.65099, p-value = 0.5151
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.05892853

Playing board games in general and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.80811, p-value = 0.419
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.0708607

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1157, p-value = 0.2645
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1023279

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1544, p-value = 0.2483
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1039873

Self efficacy and level for each task

## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 36 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.1205, p-value = 0.2625
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1226574
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 33 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.3508, p-value = 0.1768
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1562583
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 35 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.83788, p-value = 0.4021
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09546528

Risk aversion and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.52666, p-value = 0.5984
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.04333044

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.3093, p-value = 0.0009353
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2869732 
## 
## [1] "risk.av.on.level.s 0.29 0.00094 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 3.2974, p-value = 0.000976
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.2789266 
## 
## [1] "risk.av.on.level.l 0.28 0.00098 ***"

Age and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.55202, p-value = 0.5809
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04421776

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.47488, p-value = 0.6349
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03986734

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.30538, p-value = 0.7601
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02507917

Sex and level for each task

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -4.2075, p-value = 2.583e-05
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.3939997 
## 
## [1] "sexe.on.level.m -0.39 2.6e-05 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0835, p-value = 0.2786
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.1064806

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -2.5838, p-value = 0.009772
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.2485807 
## 
## [1] "sexe.on.level.l -0.25 0.0098 **"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 225, p-value = 2.648e-05
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.0356472 -0.3746198
## sample estimates:
## difference in location 
##             -0.6783409 
## 
## [1] "sexe.on.level.m.2 -0.68 2.6e-05 *** mean(A): 0.2 mean(B): -0.56"

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 439, p-value = 0.2814
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.5476773  0.1860382
## sample estimates:
## difference in location 
##             -0.1767853

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 341, p-value = 0.009943
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.4009396 -0.1989844
## sample estimates:
## difference in location 
##             -0.8165971 
## 
## [1] "sexe.on.level.l.2 -0.82 0.0099 ** mean(A): 0.2 mean(B): -0.54"

Subjective difficulty and play habits

Playing video game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.20871, p-value = 0.8347
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.01082703

Playing board game in general and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.2833, p-value = 0.1994
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.066153

In game level and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.65283, p-value = 0.5139
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.02949556

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.15101, p-value = 0.88
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01165501

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.5834, p-value = 0.1133
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.1283702

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.37801, p-value = 0.7054
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02998889

Sex and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.64555, p-value = 0.5186
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0356036

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.50129, p-value = 0.6162
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0469424

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.34017, p-value = 0.7337
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.03342996

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.25778, p-value = 0.7966
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02480039

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 4760, p-value = 0.5193
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.05522880  0.02878889
## sample estimates:
## difference in location 
##            -0.01285877

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 554, p-value = 0.6201
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.09035599  0.06089351
## sample estimates:
## difference in location 
##            -0.01840847

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 498, p-value = 0.7385
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.08529737  0.06934092
## sample estimates:
## difference in location 
##            -0.01135922

## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  B and A
## W = 535, p-value = 0.8012
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.08451038  0.07413238
## sample estimates:
## difference in location 
##           -0.009591776

Risk aversion and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.12516, p-value = 0.9004
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.006065128

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.079, p-value = 0.937
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##          tau 
## -0.006499567

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.39469, p-value = 0.6931
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.03422617

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.52264, p-value = 0.6012
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04421026

Self efficacy and subjective difficulty error

## Warning: Removed 104 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.32517, p-value = 0.7451
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.02093754
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 36 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.14142, p-value = 0.8875
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.01548103
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 33 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 0.012624, p-value = 0.9899
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## 0.001460358
## Warning in cor.test.default(Y, X, method = "kendall"): Cannot compute exact
## p-value with ties
## Warning: Removed 35 rows containing missing values (geom_point).

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -0.4493, p-value = 0.6532
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.05119152

Self efficacy (from subj diff, not quest.) and subjective difficulty error

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 18.534, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.8418912 
## 
## [1] "self.eff.subj.on.error 0.84 1.1e-76 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 10.804, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.8348064 
## 
## [1] "self.eff.subj.on.error 0.83 3.3e-27 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 10.494, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.8519049 
## 
## [1] "self.eff.subj.on.Oerror 0.85 9.2e-26 ***"

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 10.585, p-value < 2.2e-16
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## 0.8407792 
## 
## [1] "self.eff.subj.on.error 0.84 3.5e-26 ***"

Self efficacy (from subj diff, not quest.) and player level

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.0166, p-value = 0.3094
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##         tau 
## -0.04598612

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.2685, p-value = 0.2046
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.0980164

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = 1.2112, p-value = 0.2258
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## 0.09832772

## 
##  Kendall's rank correlation tau
## 
## data:  Y and X
## z = -1.5495, p-value = 0.1213
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##       tau 
## -0.123077

OLD!! We investigate the link between player’s reported game habits, feeling of self efficacy, risk aversion and player’s behavior in the different games. Feeling of self efficacy shows a small link with performance on motor task (Kendal \(\tau\)=0.26, p<0.01) and logical task (Kendal \(\tau\)=0.17, p=0.053). Aversion to risk shows a small link with performance on sensory (Kendal \(\tau\)=0.29, p<0.001) and logical task (Kendal \(\tau\)=0.27 p<0.01). In this experiment, female players tend to have a lower performance on motor (Kendal \(\tau\)=-0.4, p<0.001) and logical tasks (Kendal \(\tau\)=-0.25, p<0.01). Player’s sex is also slightly related to the error between subjective and objective difficulty (Kendal \(\tau\)=-0.19, p=0.053) i.e. compared to male players, female players tend to underestimate logical task difficulty.

Influence of Objective difficulty on Subjective Difficulty

All tasks

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125        -0.0310 27   3e-04 ***
##  2:      0.09375        -0.0220 59     0.035 *
##  3:      0.15625        -0.0250 68     0.32 :(
##  4:      0.21875        -0.0045 72     0.75 :(
##  5:      0.28125        -0.0260 77     0.29 :(
##  6:      0.34375        -0.0290 80     0.23 :(
##  7:      0.40625        -0.0340 80     0.18 :(
##  8:      0.46875        -0.0750 80   0.0067 **
##  9:      0.53125        -0.1200 80 4.8e-06 ***
## 10:      0.59375        -0.1300 78 4.8e-06 ***
## 11:      0.65625        -0.2200 79 2.4e-09 ***
## 12:      0.71875        -0.2200 76 3.1e-09 ***
## 13:      0.78125        -0.2500 51   9e-08 ***
## 14:      0.84375        -0.2400 56 3.3e-06 ***
## 15:      0.90625        -0.1900 30 1.2e-06 ***
## 16:      0.96875        -0.2300 17 0.00023 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 27   3e-04 ***
##  2: 59     0.035 *
##  3: 68     0.32 :(
##  4: 72     0.75 :(
##  5: 77     0.29 :(
##  6: 80     0.23 :(
##  7: 80     0.18 :(
##  8: 80   0.0067 **
##  9: 80 4.8e-06 ***
## 10: 78 4.8e-06 ***
## 11: 79 2.4e-09 ***
## 12: 76 3.1e-09 ***
## 13: 51   9e-08 ***
## 14: 56 3.3e-06 ***
## 15: 30 1.2e-06 ***
## 16: 17 0.00023 ***
## [1] 63.1
## [1] 21.3

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125        -0.0310 22  0.003 **
##  2:      0.09375        -0.0045 30   0.93 :(
##  3:      0.15625         0.0045 32   0.86 :(
##  4:      0.21875         0.0210 35   0.65 :(
##  5:      0.28125         0.0480 34   0.16 :(
##  6:      0.34375        -0.0200 33   0.73 :(
##  7:      0.40625        -0.0430 34   0.22 :(
##  8:      0.46875        -0.0880 34   0.073 .
##  9:      0.53125        -0.1200 33   0.017 *
## 10:      0.59375        -0.1700 33 0.0027 **
## 11:      0.65625        -0.2100 32 0.0018 **
## 12:      0.71875        -0.2200 26 0.0034 **
## 13:      0.78125        -0.2100 14   0.032 *
## 14:      0.84375        -0.3100  9   0.024 *
## 15:      0.90625        -0.1900  6   0.036 *
## 16:      0.96875             NA  4        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 22  0.003 **
##  2: 30   0.93 :(
##  3: 32   0.86 :(
##  4: 35   0.65 :(
##  5: 34   0.16 :(
##  6: 33   0.73 :(
##  7: 34   0.22 :(
##  8: 34   0.073 .
##  9: 33   0.017 *
## 10: 33 0.0027 **
## 11: 32 0.0018 **
## 12: 26 0.0034 **
## 13: 14   0.032 *
## 14:  9   0.024 *
## 15:  6   0.036 *
## [1] 27.1
## [1] 9.78
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  7          NA
##  2:      0.09375         -0.094 44 0.00048 ***
##  3:      0.15625         -0.044 42     0.13 :(
##  4:      0.21875         -0.076 48     0.081 .
##  5:      0.28125         -0.072 60     0.012 *
##  6:      0.34375         -0.042 66      0.1 :(
##  7:      0.40625         -0.037 67     0.29 :(
##  8:      0.46875         -0.088 64     0.022 *
##  9:      0.53125         -0.140 65 9.8e-05 ***
## 10:      0.59375         -0.170 62 0.00029 ***
## 11:      0.65625         -0.230 59 2.6e-06 ***
## 12:      0.71875         -0.220 52 4.8e-05 ***
## 13:      0.78125         -0.210 33 4.1e-05 ***
## 14:      0.84375         -0.240 30 0.00025 ***
## 15:      0.90625         -0.160 17 0.00023 ***
## 16:      0.96875         -0.330  9    0.008 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 44 0.00048 ***
##  2: 42     0.13 :(
##  3: 48     0.081 .
##  4: 60     0.012 *
##  5: 66      0.1 :(
##  6: 67     0.29 :(
##  7: 64     0.022 *
##  8: 65 9.8e-05 ***
##  9: 62 0.00029 ***
## 10: 59 2.6e-06 ***
## 11: 52 4.8e-05 ***
## 12: 33 4.1e-05 ***
## 13: 30 0.00025 ***
## 14: 17 0.00023 ***
## 15:  9    0.008 **
## [1] 47.9
## [1] 18.5
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  7          NA
##  3:      0.15625         -0.057 23     0.092 .
##  4:      0.21875         -0.076 21     0.25 :(
##  5:      0.28125         -0.041 32     0.38 :(
##  6:      0.34375         -0.076 33     0.086 .
##  7:      0.40625         -0.049 33     0.25 :(
##  8:      0.46875         -0.130 36   0.0067 **
##  9:      0.53125         -0.087 36     0.014 *
## 10:      0.59375         -0.140 34   0.0041 **
## 11:      0.65625         -0.230 32 2.9e-05 ***
## 12:      0.71875         -0.240 33 0.00014 ***
## 13:      0.78125         -0.280 19 0.00083 ***
## 14:      0.84375         -0.220 20     0.14 :(
## 15:      0.90625         -0.220  7      0.02 *
## 16:      0.96875         -0.160  4     0.098 .
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 23     0.092 .
##  2: 21     0.25 :(
##  3: 32     0.38 :(
##  4: 33     0.086 .
##  5: 33     0.25 :(
##  6: 36   0.0067 **
##  7: 36     0.014 *
##  8: 34   0.0041 **
##  9: 32 2.9e-05 ***
## 10: 33 0.00014 ***
## 11: 19 0.00083 ***
## 12: 20     0.14 :(
## 13:  7      0.02 *
## 14:  4     0.098 .
## [1] 25.9
## [1] 10.5
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Motor task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  3          NA
##  3:      0.15625        -0.0130 13     0.78 :(
##  4:      0.21875        -0.0045 29     0.21 :(
##  5:      0.28125        -0.0310 61      0.5 :(
##  6:      0.34375        -0.0720 73     0.025 *
##  7:      0.40625        -0.0810 74     0.025 *
##  8:      0.46875        -0.1000 74   0.0053 **
##  9:      0.53125        -0.1300 76 0.00052 ***
## 10:      0.59375        -0.2000 72 2.8e-06 ***
## 11:      0.65625        -0.2500 61 2.2e-06 ***
## 12:      0.71875        -0.2900 38 1.6e-05 ***
## 13:      0.78125        -0.4100 11   0.0065 **
## 14:      0.84375        -0.5400  4      0.2 :(
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 13     0.78 :(
##  2: 29     0.21 :(
##  3: 61      0.5 :(
##  4: 73     0.025 *
##  5: 74     0.025 *
##  6: 74   0.0053 **
##  7: 76 0.00052 ***
##  8: 72 2.8e-06 ***
##  9: 61 2.2e-06 ***
## 10: 38 1.6e-05 ***
## 11: 11   0.0065 **
## 12:  4      0.2 :(
## [1] 48.8
## [1] 28.1
## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 4 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  3      NA
##  3:      0.15625         -0.013 10 0.76 :(
##  4:      0.21875          0.019 18 0.79 :(
##  5:      0.28125          0.076 19 0.095 .
##  6:      0.34375          0.031 19 0.59 :(
##  7:      0.40625         -0.035 19 0.56 :(
##  8:      0.46875         -0.064 19 0.36 :(
##  9:      0.53125         -0.100 18 0.12 :(
## 10:      0.59375         -0.130 17 0.072 .
## 11:      0.65625         -0.220 12 0.054 .
## 12:      0.71875         -0.230  7 0.55 :(
## 13:      0.78125             NA  1      NA
## 14:      0.84375             NA  0      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 10 0.76 :(
##  2: 18 0.79 :(
##  3: 19 0.095 .
##  4: 19 0.59 :(
##  5: 19 0.56 :(
##  6: 19 0.36 :(
##  7: 18 0.12 :(
##  8: 17 0.072 .
##  9: 12 0.054 .
## 10:  7 0.55 :(
## [1] 15.8
## [1] 4.44
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  0          NA
##  3:      0.15625             NA  3          NA
##  4:      0.21875         -0.220 11      0.07 .
##  5:      0.28125         -0.067 42     0.16 :(
##  6:      0.34375         -0.084 49     0.031 *
##  7:      0.40625         -0.089 49     0.035 *
##  8:      0.46875         -0.110 49     0.018 *
##  9:      0.53125         -0.140 49   0.0032 **
## 10:      0.59375         -0.240 45 5.8e-05 ***
## 11:      0.65625         -0.260 39 0.00018 ***
## 12:      0.71875         -0.380 23 0.00024 ***
## 13:      0.78125         -0.410  6     0.031 *
## 14:      0.84375             NA  1          NA
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 11      0.07 .
##  2: 42     0.16 :(
##  3: 49     0.031 *
##  4: 49     0.035 *
##  5: 49     0.018 *
##  6: 49   0.0032 **
##  7: 45 5.8e-05 ***
##  8: 39 0.00018 ***
##  9: 23 0.00024 ***
## 10:  6     0.031 *
## [1] 36.2
## [1] 16.7
## Warning: Removed 6 rows containing missing values (geom_point).
## Warning: Removed 6 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  0      NA
##  3:      0.15625             NA  0      NA
##  4:      0.21875             NA  0      NA
##  5:      0.28125             NA  0      NA
##  6:      0.34375             NA  5      NA
##  7:      0.40625          -0.09  6 0.44 :(
##  8:      0.46875          -0.14  6 0.31 :(
##  9:      0.53125          -0.12  9 0.41 :(
## 10:      0.59375          -0.18 10 0.15 :(
## 11:      0.65625          -0.30 10 0.041 *
## 12:      0.71875          -0.29  8 0.042 *
## 13:      0.78125          -0.57  4 0.095 .
## 14:      0.84375             NA  3      NA
## 15:      0.90625             NA  0      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##    nb    pval
## 1:  6 0.44 :(
## 2:  6 0.31 :(
## 3:  9 0.41 :(
## 4: 10 0.15 :(
## 5: 10 0.041 *
## 6:  8 0.042 *
## 7:  4 0.095 .
## [1] 7.57
## [1] 2.3
## Warning: Removed 9 rows containing missing values (geom_point).
## Warning: Removed 9 rows containing missing values (geom_errorbar).

Sensory task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA 15          NA
##  2:      0.09375         -0.094 54 5.7e-07 ***
##  3:      0.15625         -0.095 61 1.7e-06 ***
##  4:      0.21875         -0.100 43 0.00062 ***
##  5:      0.28125         -0.094 51     0.017 *
##  6:      0.34375         -0.120 46   0.0058 **
##  7:      0.40625         -0.049 41     0.44 :(
##  8:      0.46875         -0.110 45   0.0057 **
##  9:      0.53125         -0.150 48 0.00038 ***
## 10:      0.59375         -0.079 37     0.094 .
## 11:      0.65625         -0.140 40   0.0036 **
## 12:      0.71875         -0.180 53 4.8e-05 ***
## 13:      0.78125         -0.140 33 0.00023 ***
## 14:      0.84375         -0.150 44   0.0018 **
## 15:      0.90625         -0.160 29 1.7e-06 ***
## 16:      0.96875         -0.230 17 0.00023 ***
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 54 5.7e-07 ***
##  2: 61 1.7e-06 ***
##  3: 43 0.00062 ***
##  4: 51     0.017 *
##  5: 46   0.0058 **
##  6: 41     0.44 :(
##  7: 45   0.0057 **
##  8: 48 0.00038 ***
##  9: 37     0.094 .
## 10: 40   0.0036 **
## 11: 53 4.8e-05 ***
## 12: 33 0.00023 ***
## 13: 44   0.0018 **
## 14: 29 1.7e-06 ***
## 15: 17 0.00023 ***
## [1] 42.8
## [1] 10.9
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj n    pval
##  1:      0.03125             NA 9      NA
##  2:      0.09375         -0.094 9 0.13 :(
##  3:      0.15625             NA 9      NA
##  4:      0.21875         -0.160 5 0.054 .
##  5:      0.28125         -0.067 5 0.31 :(
##  6:      0.34375         -0.085 4 0.38 :(
##  7:      0.40625         -0.049 5    1 :(
##  8:      0.46875         -0.100 5 0.81 :(
##  9:      0.53125         -0.200 5 0.44 :(
## 10:      0.59375         -0.097 4 0.88 :(
## 11:      0.65625         -0.013 6    1 :(
## 12:      0.71875         -0.270 7 0.075 .
## 13:      0.78125         -0.085 4 0.58 :(
## 14:      0.84375         -0.200 6 0.14 :(
## 15:      0.90625         -0.190 6 0.036 *
## 16:      0.96875             NA 4      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1:  9 0.13 :(
##  2:  5 0.054 .
##  3:  5 0.31 :(
##  4:  4 0.38 :(
##  5:  5    1 :(
##  6:  5 0.81 :(
##  7:  5 0.44 :(
##  8:  4 0.88 :(
##  9:  6    1 :(
## 10:  7 0.075 .
## 11:  4 0.58 :(
## 12:  6 0.14 :(
## 13:  6 0.036 *
## [1] 5.46
## [1] 1.39
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  6          NA
##  2:      0.09375         -0.094 38 2.2e-06 ***
##  3:      0.15625         -0.110 32 0.00024 ***
##  4:      0.21875         -0.150 24 0.00073 ***
##  5:      0.28125         -0.110 25     0.036 *
##  6:      0.34375         -0.094 27     0.055 .
##  7:      0.40625         -0.085 24     0.27 :(
##  8:      0.46875         -0.120 22     0.055 .
##  9:      0.53125         -0.180 27   0.0032 **
## 10:      0.59375         -0.130 22     0.038 *
## 11:      0.65625         -0.270 20 0.00072 ***
## 12:      0.71875         -0.170 26   0.0041 **
## 13:      0.78125         -0.210 21 0.00093 ***
## 14:      0.84375         -0.130 23   0.0046 **
## 15:      0.90625         -0.120 16 0.00033 ***
## 16:      0.96875         -0.330  9    0.008 **
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 38 2.2e-06 ***
##  2: 32 0.00024 ***
##  3: 24 0.00073 ***
##  4: 25     0.036 *
##  5: 27     0.055 .
##  6: 24     0.27 :(
##  7: 22     0.055 .
##  8: 27   0.0032 **
##  9: 22     0.038 *
## 10: 20 0.00072 ***
## 11: 26   0.0041 **
## 12: 21 0.00093 ***
## 13: 23   0.0046 **
## 14: 16 0.00033 ***
## 15:  9    0.008 **
## [1] 23.7
## [1] 6.57
## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  0      NA
##  2:      0.09375             NA  7      NA
##  3:      0.15625        -0.0540 20 0.12 :(
##  4:      0.21875        -0.0044 14 0.95 :(
##  5:      0.28125        -0.0790 21 0.27 :(
##  6:      0.34375        -0.1300 15  0.1 :(
##  7:      0.40625         0.0220 12 0.56 :(
##  8:      0.46875        -0.1300 18 0.081 .
##  9:      0.53125        -0.0870 16 0.093 .
## 10:      0.59375         0.0130 11 0.89 :(
## 11:      0.65625        -0.0130 14 0.66 :(
## 12:      0.71875        -0.2000 20 0.038 *
## 13:      0.78125        -0.1400  8 0.23 :(
## 14:      0.84375        -0.1700 15 0.79 :(
## 15:      0.90625        -0.2200  7  0.02 *
## 16:      0.96875        -0.1600  4 0.098 .
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 20 0.12 :(
##  2: 14 0.95 :(
##  3: 21 0.27 :(
##  4: 15  0.1 :(
##  5: 12 0.56 :(
##  6: 18 0.081 .
##  7: 16 0.093 .
##  8: 11 0.89 :(
##  9: 14 0.66 :(
## 10: 20 0.038 *
## 11:  8 0.23 :(
## 12: 15 0.79 :(
## 13:  7  0.02 *
## 14:  4 0.098 .
## [1] 13.9
## [1] 5.12
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

Logical task

## [1] "all"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125         -0.031 17     0.027 *
##  2:      0.09375          0.025 38     0.69 :(
##  3:      0.15625          0.058 51     0.15 :(
##  4:      0.21875          0.067 61     0.17 :(
##  5:      0.28125          0.040 66     0.11 :(
##  6:      0.34375          0.034 71      0.4 :(
##  7:      0.40625          0.010 71     0.83 :(
##  8:      0.46875         -0.062 72     0.054 .
##  9:      0.53125         -0.091 73     0.014 *
## 10:      0.59375         -0.130 67   0.0016 **
## 11:      0.65625         -0.230 63 4.3e-07 ***
## 12:      0.71875         -0.210 52 1.7e-05 ***
## 13:      0.78125         -0.280 30 0.00012 ***
## 14:      0.84375         -0.420 14   0.0012 **
## 15:      0.90625             NA  1          NA
## 16:      0.96875             NA  0          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1: 17     0.027 *
##  2: 38     0.69 :(
##  3: 51     0.15 :(
##  4: 61     0.17 :(
##  5: 66     0.11 :(
##  6: 71      0.4 :(
##  7: 71     0.83 :(
##  8: 72     0.054 .
##  9: 73     0.014 *
## 10: 67   0.0016 **
## 11: 63 4.3e-07 ***
## 12: 52 1.7e-05 ***
## 13: 30 0.00012 ***
## 14: 14   0.0012 **
## [1] 53.3
## [1] 20.6
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## [1] "good"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n      pval
##  1:      0.03125         -0.031 16    0.04 *
##  2:      0.09375          0.025 25   0.76 :(
##  3:      0.15625          0.130 25   0.23 :(
##  4:      0.21875          0.100 25   0.11 :(
##  5:      0.28125          0.040 24   0.47 :(
##  6:      0.34375         -0.058 25   0.48 :(
##  7:      0.40625         -0.049 24   0.38 :(
##  8:      0.46875         -0.110 25   0.021 *
##  9:      0.53125         -0.120 24   0.047 *
## 10:      0.59375         -0.170 23 0.0048 **
## 11:      0.65625         -0.230 22  0.002 **
## 12:      0.71875         -0.290 19  0.002 **
## 13:      0.78125         -0.280 10   0.041 *
## 14:      0.84375         -0.450  4   0.098 .
## 15:      0.90625             NA  0        NA
## 16:      0.96875             NA  0        NA
## [1] "mean and sd of nb players per bin"
##     nb      pval
##  1: 16    0.04 *
##  2: 25   0.76 :(
##  3: 25   0.23 :(
##  4: 25   0.11 :(
##  5: 24   0.47 :(
##  6: 25   0.48 :(
##  7: 24   0.38 :(
##  8: 25   0.021 *
##  9: 24   0.047 *
## 10: 23 0.0048 **
## 11: 22  0.002 **
## 12: 19  0.002 **
## 13: 10   0.041 *
## 14:  4   0.098 .
## [1] 20.8
## [1] 6.51
## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## [1] "medium"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties

##     obj.diff.bin delta.obj.subj  n    pval
##  1:      0.03125             NA  1      NA
##  2:      0.09375          0.013 13 0.83 :(
##  3:      0.15625          0.058 22 0.23 :(
##  4:      0.21875          0.140 24 0.083 .
##  5:      0.28125          0.058 24 0.14 :(
##  6:      0.34375          0.150 24 0.038 *
##  7:      0.40625          0.094 23 0.091 .
##  8:      0.46875          0.031 23  0.7 :(
##  9:      0.53125         -0.013 24 0.92 :(
## 10:      0.59375         -0.022 22 0.72 :(
## 11:      0.65625         -0.130 22 0.096 .
## 12:      0.71875         -0.040 19 0.36 :(
## 13:      0.78125         -0.170 11 0.067 .
## 14:      0.84375         -0.430  7 0.034 *
## 15:      0.90625             NA  1      NA
## 16:      0.96875             NA  0      NA
## [1] "mean and sd of nb players per bin"
##     nb    pval
##  1: 13 0.83 :(
##  2: 22 0.23 :(
##  3: 24 0.083 .
##  4: 24 0.14 :(
##  5: 24 0.038 *
##  6: 23 0.091 .
##  7: 23  0.7 :(
##  8: 24 0.92 :(
##  9: 22 0.72 :(
## 10: 22 0.096 .
## 11: 19 0.36 :(
## 12: 11 0.067 .
## 13:  7 0.034 *
## [1] 19.8
## [1] 5.73
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_errorbar).

## [1] "bad"
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact p-value with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): cannot compute exact confidence interval with ties
## Warning in wilcox.test.default(subj.diff, mu = obj.diff.bin.cur, conf.int =
## T): requested conf.level not achievable

##     obj.diff.bin delta.obj.subj  n        pval
##  1:      0.03125             NA  0          NA
##  2:      0.09375             NA  0          NA
##  3:      0.15625         -0.060  4     0.36 :(
##  4:      0.21875         -0.160 12     0.21 :(
##  5:      0.28125          0.033 18     0.51 :(
##  6:      0.34375         -0.010 22     0.82 :(
##  7:      0.40625         -0.013 24     0.68 :(
##  8:      0.46875         -0.088 24     0.079 .
##  9:      0.53125         -0.110 25     0.038 *
## 10:      0.59375         -0.200 22      0.01 *
## 11:      0.65625         -0.290 19 0.00023 ***
## 12:      0.71875         -0.290 14   0.0024 **
## 13:      0.78125         -0.350  9   0.0084 **
## 14:      0.84375             NA  3          NA
## 15:      0.90625             NA  0          NA
## 16:      0.96875             NA  0          NA
## [1] "mean and sd of nb players per bin"
##     nb        pval
##  1:  4     0.36 :(
##  2: 12     0.21 :(
##  3: 18     0.51 :(
##  4: 22     0.82 :(
##  5: 24     0.68 :(
##  6: 24     0.079 .
##  7: 25     0.038 *
##  8: 22      0.01 *
##  9: 19 0.00023 ***
## 10: 14   0.0024 **
## 11:  9   0.0084 **
## [1] 17.5
## [1] 6.93
## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_errorbar).

Influence of Playtime on Subjective Difficulty Error

For all groups, motor, sensitive and logical

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.47512  -0.32099   0.00198   0.28034   0.62514  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17124    0.02662   6.432 1.52e-10 ***
## timeNorm     0.01183    0.02348   0.504    0.614    
## obj.diff    -0.63770    0.05194 -12.277  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1062912)
## 
##     Null deviance: 264.48  on 2339  degrees of freedom
## Residual deviance: 248.40  on 2337  degrees of freedom
## AIC: 1400.3
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.73012  -0.23623  -0.06378   0.27512   0.77690  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.03288    0.01832  -1.795   0.0728 .  
## timeNorm     0.05464    0.02474   2.209   0.0273 *  
## obj.diff    -0.23453    0.03151  -7.443 1.43e-13 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1028656)
## 
##     Null deviance: 224.52  on 2129  degrees of freedom
## Residual deviance: 218.80  on 2127  degrees of freedom
## AIC: 1205.3
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL)
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.57929  -0.31805  -0.00183   0.31692   0.63841  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.17798    0.01962   9.072  < 2e-16 ***
## timeNorm     0.07804    0.02588   3.016  0.00259 ** 
## obj.diff    -0.60382    0.04194 -14.398  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1105958)
## 
##     Null deviance: 268.47  on 2219  degrees of freedom
## Residual deviance: 245.19  on 2217  degrees of freedom
## AIC: 1416.9
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2887668     0.4250930 -0.13953917 234 8.3e-09 ***
##  2:      4.5      0.3577534     0.4490995 -0.09225023 234 0.00012 ***
##  3:      7.5      0.3736264     0.4588281 -0.08415455 234   0.0011 **
##  4:     10.5      0.3443223     0.4590774 -0.11697149 234 1.4e-06 ***
##  5:     13.5      0.3388278     0.4603425 -0.12311547 234 1.6e-07 ***
##  6:     16.5      0.3504274     0.4656950 -0.11694812 234 1.1e-06 ***
##  7:     19.5      0.3284493     0.4694363 -0.14111674 234 1.2e-09 ***
##  8:     22.5      0.3431013     0.4793797 -0.14609825 234 4.7e-09 ***
##  9:     25.5      0.3833944     0.4792794 -0.09957072 234 0.00013 ***
## 10:     28.5      0.3363858     0.4675171 -0.12098146 234 4.9e-08 ***
##     time  error.diff shapes
##  1:  1.5 -0.13953917     24
##  2:  4.5 -0.09225023     24
##  3:  7.5 -0.08415455     24
##  4: 10.5 -0.11697149     24
##  5: 13.5 -0.12311547     24
##  6: 16.5 -0.11694812     24
##  7: 19.5 -0.14111674     24
##  8: 22.5 -0.14609825     24
##  9: 25.5 -0.09957072     24
## 10: 28.5 -0.12098146     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1368209     0.2153795 -0.10692408 213 3.2e-08 ***
##  2:      4.5      0.2964453     0.4306730 -0.15011494 213   3e-09 ***
##  3:      7.5      0.3333333     0.4960787 -0.16696074 213 9.6e-11 ***
##  4:     10.5      0.3521127     0.4942637 -0.14997408 213 5.9e-09 ***
##  5:     13.5      0.3876593     0.4785235 -0.09232594 213   1e-04 ***
##  6:     16.5      0.3970490     0.4910775 -0.09082835 213 0.00014 ***
##  7:     19.5      0.3816231     0.4794597 -0.10563208 213 1.2e-07 ***
##  8:     22.5      0.4084507     0.5148548 -0.10674468 213 8.5e-05 ***
##  9:     25.5      0.4151576     0.4815308 -0.06349500 213     0.011 *
## 10:     28.5      0.3474178     0.4938488 -0.15316819 213 1.4e-08 ***
##     time  error.diff shapes
##  1:  1.5 -0.10692408     24
##  2:  4.5 -0.15011494     24
##  3:  7.5 -0.16696074     24
##  4: 10.5 -0.14997408     24
##  5: 13.5 -0.09232594     24
##  6: 16.5 -0.09082835     24
##  7: 19.5 -0.10563208     24
##  8: 22.5 -0.10674468     24
##  9: 25.5 -0.06349500     24
## 10: 28.5 -0.15316819     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n      pval
##  1:      1.5      0.2149292     0.2543626 -0.07057268 222 0.0012 **
##  2:      4.5      0.3558559     0.3238165  0.03016309 222   0.24 :(
##  3:      7.5      0.3873874     0.4029150 -0.01604677 222   0.54 :(
##  4:     10.5      0.3925354     0.4338872 -0.03958987 222    0.1 :(
##  5:     13.5      0.4118404     0.4496167 -0.04062829 222   0.11 :(
##  6:     16.5      0.4227799     0.4766409 -0.05128067 222   0.032 *
##  7:     19.5      0.4111969     0.4797636 -0.07613895 222 0.0041 **
##  8:     22.5      0.3880309     0.4387192 -0.05567027 222   0.025 *
##  9:     25.5      0.4182754     0.4588002 -0.04151817 222   0.098 .
## 10:     28.5      0.4427284     0.4780249 -0.03297758 222   0.24 :(
##     time  error.diff shapes
##  1:  1.5 -0.07057268     24
##  2:  4.5  0.03016309     16
##  3:  7.5 -0.01604677     16
##  4: 10.5 -0.03958987     16
##  5: 13.5 -0.04062829     16
##  6: 16.5 -0.05128067     24
##  7: 19.5 -0.07613895     24
##  8: 22.5 -0.05567027     24
##  9: 25.5 -0.04151817     16
## 10: 28.5 -0.03297758     16

For all taks, per group

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.64070  -0.29956  -0.02893   0.31784   0.69906  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.07365    0.02846   2.588  0.00973 ** 
## timeNorm     0.03582    0.02788   1.285  0.19900    
## obj.diff    -0.42758    0.04795  -8.917  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1143242)
## 
##     Null deviance: 211.41  on 1769  degrees of freedom
## Residual deviance: 202.01  on 1767  degrees of freedom
## AIC: 1189.4
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.65216  -0.30312  -0.03606   0.30067   0.70453  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.05837    0.01763   3.310 0.000943 ***
## timeNorm     0.08051    0.02040   3.947 8.07e-05 ***
## obj.diff    -0.42170    0.03315 -12.722  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1116797)
## 
##     Null deviance: 389.96  on 3329  degrees of freedom
## Residual deviance: 371.56  on 3327  degrees of freedom
## AIC: 2155.4
## 
## Number of Fisher Scoring iterations: 2

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTAll[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.61482  -0.25581  -0.02273   0.26062   0.68726  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.12404    0.01869   6.637 4.39e-11 ***
## timeNorm    -0.03577    0.03222  -1.110    0.267    
## obj.diff    -0.40591    0.04638  -8.753  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09536639)
## 
##     Null deviance: 163.52  on 1589  degrees of freedom
## Residual deviance: 151.35  on 1587  degrees of freedom
## AIC: 780.68
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2857143     0.4423894 -0.16573403 177 3.1e-08 ***
##  2:      4.5      0.3922518     0.4896586 -0.09931788 177 0.00068 ***
##  3:      7.5      0.3979015     0.5068043 -0.10894563 177 0.00018 ***
##  4:     10.5      0.3454399     0.4841123 -0.14127326 177 3.9e-07 ***
##  5:     13.5      0.3761098     0.4789837 -0.10454587 177   4e-04 ***
##  6:     16.5      0.3825666     0.4794269 -0.09760148 177 0.00024 ***
##  7:     19.5      0.3373688     0.4690341 -0.14526558 177 2.2e-06 ***
##  8:     22.5      0.3704600     0.4630634 -0.09670615 177 0.00066 ***
##  9:     25.5      0.3672316     0.4363366 -0.07355281 177     0.011 *
## 10:     28.5      0.3656174     0.4653488 -0.10252754 177 0.00058 ***
##     time  error.diff shapes
##  1:  1.5 -0.16573403     24
##  2:  4.5 -0.09931788     24
##  3:  7.5 -0.10894563     24
##  4: 10.5 -0.14127326     24
##  5: 13.5 -0.10454587     24
##  6: 16.5 -0.09760148     24
##  7: 19.5 -0.14526558     24
##  8: 22.5 -0.09670615     24
##  9: 25.5 -0.07355281     24
## 10: 28.5 -0.10252754     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.2183612     0.3061629 -0.11151391 333 2.9e-09 ***
##  2:      4.5      0.3389103     0.4353812 -0.10425411 333 4.6e-07 ***
##  3:      7.5      0.3474903     0.4722862 -0.12798535 333 5.2e-09 ***
##  4:     10.5      0.3603604     0.4737386 -0.11826590 333 4.1e-08 ***
##  5:     13.5      0.3706564     0.4485476 -0.08256362 333 5.3e-05 ***
##  6:     16.5      0.3865294     0.4681791 -0.08059753 333   6e-05 ***
##  7:     19.5      0.3912484     0.4725740 -0.08768249 333   4e-06 ***
##  8:     22.5      0.3813814     0.4750074 -0.09779339 333 3.6e-06 ***
##  9:     25.5      0.4268554     0.4779570 -0.05324531 333     0.016 *
## 10:     28.5      0.3792364     0.4684390 -0.08597398 333   6e-06 ***
##     time  error.diff shapes
##  1:  1.5 -0.11151391     24
##  2:  4.5 -0.10425411     24
##  3:  7.5 -0.12798535     24
##  4: 10.5 -0.11826590     24
##  5: 13.5 -0.08256362     24
##  6: 16.5 -0.08059753     24
##  7: 19.5 -0.08768249     24
##  8: 22.5 -0.09779339     24
##  9: 25.5 -0.05324531     24
## 10: 28.5 -0.08597398     24

##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.1329739     0.1356034 -0.04799946 159   0.0086 **
##  2:      4.5      0.2740341     0.2330713  0.02295544 159     0.35 :(
##  3:      7.5      0.3665768     0.3490692  0.01576858 159     0.59 :(
##  4:     10.5      0.3872417     0.4124680 -0.02740123 159     0.31 :(
##  5:     13.5      0.3980234     0.4736736 -0.08042557 159   0.0021 **
##  6:     16.5      0.4025157     0.4944921 -0.08989010 159   0.0011 **
##  7:     19.5      0.3737646     0.4911595 -0.11967220 159 1.8e-05 ***
##  8:     22.5      0.3827493     0.4974522 -0.11798555 159   8e-05 ***
##  9:     25.5      0.4016173     0.5042757 -0.10492584 159 0.00099 ***
## 10:     28.5      0.3773585     0.5179459 -0.14347107 159 1.5e-05 ***
##     time  error.diff shapes
##  1:  1.5 -0.04799946     24
##  2:  4.5  0.02295544     16
##  3:  7.5  0.01576858     16
##  4: 10.5 -0.02740123     16
##  5: 13.5 -0.08042557     24
##  6: 16.5 -0.08989010     24
##  7: 19.5 -0.11967220     24
##  8: 22.5 -0.11798555     24
##  9: 25.5 -0.10492584     24
## 10: 28.5 -0.14347107     24

Per group, motor task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.43457  -0.35583  -0.02824   0.36659   0.56412  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)   
## (Intercept)  0.14151    0.13753   1.029  0.30435   
## timeNorm     0.04168    0.08032   0.519  0.60420   
## obj.diff    -0.65302    0.20479  -3.189  0.00158 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1326164)
## 
##     Null deviance: 41.293  on 299  degrees of freedom
## Residual deviance: 39.387  on 297  degrees of freedom
## AIC: 250.26
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean error.diff  n        pval
##  1:      1.5      0.3047619     0.6877076 -0.3682682 30 0.00011 ***
##  2:      4.5      0.4285714     0.5945163 -0.1791742 30      0.08 .
##  3:      7.5      0.3857143     0.5886926 -0.2205960 30      0.04 *
##  4:     10.5      0.3571429     0.5510796 -0.1861968 30     0.014 *
##  5:     13.5      0.3666667     0.5320759 -0.1609103 30     0.016 *
##  6:     16.5      0.3238095     0.5154227 -0.2109705 30   0.0058 **
##  7:     19.5      0.2952381     0.5359163 -0.2485029 30   0.0022 **
##  8:     22.5      0.3523810     0.5143672 -0.1878073 30   0.0093 **
##  9:     25.5      0.3714286     0.4954621 -0.1366127 30     0.052 .
## 10:     28.5      0.3619048     0.5099490 -0.1453273 30      0.05 .
##     time error.diff shapes
##  1:  1.5 -0.3682682     24
##  2:  4.5 -0.1791742     16
##  3:  7.5 -0.2205960     24
##  4: 10.5 -0.1861968     24
##  5: 13.5 -0.1609103     24
##  6: 16.5 -0.2109705     24
##  7: 19.5 -0.2485029     24
##  8: 22.5 -0.1878073     24
##  9: 25.5 -0.1366127     16
## 10: 28.5 -0.1453273     24
## Warning: Removed 2 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.47424  -0.30992  -0.01402   0.28477   0.61857  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.11471    0.03823   3.001  0.00274 ** 
## timeNorm     0.04259    0.02942   1.448  0.14788    
## obj.diff    -0.57219    0.07270  -7.871 6.79e-15 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1057341)
## 
##     Null deviance: 161.99  on 1469  degrees of freedom
## Residual deviance: 155.11  on 1467  degrees of freedom
## AIC: 873.84
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5      0.3080661     0.4453946 -0.14345456 147 9.3e-07 ***
##  2:      4.5      0.3362488     0.4739898 -0.14005322 147 1.2e-06 ***
##  3:      7.5      0.3479106     0.4707060 -0.12577393 147   7e-05 ***
##  4:     10.5      0.3168124     0.4718000 -0.15743110 147 1.6e-07 ***
##  5:     13.5      0.3275024     0.4718746 -0.14858936 147 1.2e-06 ***
##  6:     16.5      0.3294461     0.4713130 -0.14395563 147 3.2e-06 ***
##  7:     19.5      0.3177843     0.4401750 -0.12309216 147 7.2e-06 ***
##  8:     22.5      0.3245870     0.4501494 -0.14007727 147 1.6e-05 ***
##  9:     25.5      0.3819242     0.4605053 -0.08422399 147     0.015 *
## 10:     28.5      0.3459670     0.4466711 -0.08846893 147 0.00035 ***
##     time  error.diff shapes
##  1:  1.5 -0.14345456     24
##  2:  4.5 -0.14005322     24
##  3:  7.5 -0.12577393     24
##  4: 10.5 -0.15743110     24
##  5: 13.5 -0.14858936     24
##  6: 16.5 -0.14395563     24
##  7: 19.5 -0.12309216     24
##  8: 22.5 -0.14007727     24
##  9: 25.5 -0.08422399     24
## 10: 28.5 -0.08846893     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTM[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.5243  -0.2887   0.0170   0.2472   0.6074  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.19432    0.03803   5.110 4.42e-07 ***
## timeNorm    -0.16087    0.05554  -2.897 0.003919 ** 
## obj.diff    -0.38512    0.10925  -3.525 0.000458 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09099729)
## 
##     Null deviance: 56.480  on 569  degrees of freedom
## Residual deviance: 51.595  on 567  degrees of freedom
## AIC: 256.33
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n        pval
##  1:      1.5      0.2305764     0.2345181 -0.022364755 57     0.59 :(
##  2:      4.5      0.3759398     0.3083734  0.059567775 57     0.083 .
##  3:      7.5      0.4335840     0.3598460  0.088313450 57     0.068 .
##  4:     10.5      0.4085213     0.3778445  0.026989430 57     0.57 :(
##  5:     13.5      0.3533835     0.3928473 -0.037906228 57     0.35 :(
##  6:     16.5      0.4185464     0.4250343 -0.003127012 57     0.94 :(
##  7:     19.5      0.3734336     0.5099102 -0.128686755 57   0.0067 **
##  8:     22.5      0.3859649     0.5363487 -0.151942393 57   0.0033 **
##  9:     25.5      0.3934837     0.5191794 -0.124405638 57     0.013 *
## 10:     28.5      0.2982456     0.4989451 -0.208899524 57 0.00012 ***
##     time   error.diff shapes
##  1:  1.5 -0.022364755     16
##  2:  4.5  0.059567775     16
##  3:  7.5  0.088313450     16
##  4: 10.5  0.026989430     16
##  5: 13.5 -0.037906228     16
##  6: 16.5 -0.003127012     16
##  7: 19.5 -0.128686755     24
##  8: 22.5 -0.151942393     24
##  9: 25.5 -0.124405638     24
## 10: 28.5 -0.208899524     24

Per group, sensory task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.7257  -0.2678  -0.1262   0.3162   0.7323  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.01988    0.03634   0.547    0.585    
## timeNorm     0.04587    0.04484   1.023    0.307    
## obj.diff    -0.28844    0.06270  -4.600 4.99e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1187679)
## 
##     Null deviance: 87.696  on 719  degrees of freedom
## Residual deviance: 85.157  on 717  degrees of freedom
## AIC: 514.24
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2281746     0.3097399 -0.10902976 72   0.038 *
##  2:      4.5      0.3630952     0.4426004 -0.08561359 72   0.053 .
##  3:      7.5      0.3630952     0.4724575 -0.11685188 72 0.0061 **
##  4:     10.5      0.3392857     0.4525641 -0.12298405 72 0.0052 **
##  5:     13.5      0.3670635     0.4656216 -0.09769915 72   0.047 *
##  6:     16.5      0.4087302     0.4811146 -0.06977024 72   0.061 .
##  7:     19.5      0.3392857     0.4360550 -0.11830674 72   0.011 *
##  8:     22.5      0.4365079     0.4764586 -0.03047354 72   0.52 :(
##  9:     25.5      0.3769841     0.4251328 -0.04729018 72   0.27 :(
## 10:     28.5      0.3710317     0.4756434 -0.11448523 72   0.021 *
##     time  error.diff shapes
##  1:  1.5 -0.10902976     24
##  2:  4.5 -0.08561359     16
##  3:  7.5 -0.11685188     24
##  4: 10.5 -0.12298405     24
##  5: 13.5 -0.09769915     24
##  6: 16.5 -0.06977024     16
##  7: 19.5 -0.11830674     24
##  8: 22.5 -0.03047354     16
##  9: 25.5 -0.04729018     16
## 10: 28.5 -0.11448523     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70507  -0.22068  -0.04335   0.24408   0.76625  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -0.06325    0.02388  -2.648  0.00820 ** 
## timeNorm     0.08543    0.03269   2.613  0.00909 ** 
## obj.diff    -0.23906    0.04135  -5.782 9.53e-09 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09501969)
## 
##     Null deviance: 111.35  on 1139  degrees of freedom
## Residual deviance: 108.04  on 1137  degrees of freedom
## AIC: 556.99
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff   n        pval
##  1:      1.5     0.09899749     0.1876252 -0.11039497 114 1.2e-07 ***
##  2:      4.5     0.28446115     0.4616587 -0.18406741 114 2.4e-08 ***
##  3:      7.5     0.30325815     0.5145617 -0.21299544 114 2.4e-09 ***
##  4:     10.5     0.33583960     0.4960885 -0.16726069 114   2e-06 ***
##  5:     13.5     0.34085213     0.4349206 -0.09783900 114 0.00087 ***
##  6:     16.5     0.37468672     0.4700852 -0.09179220 114   0.0085 **
##  7:     19.5     0.41102757     0.5126837 -0.10061540 114 4.9e-06 ***
##  8:     22.5     0.39097744     0.5324474 -0.14712519 114 0.00012 ***
##  9:     25.5     0.44987469     0.5092968 -0.05935521 114      0.1 :(
## 10:     28.5     0.32957393     0.4942599 -0.17055244 114 9.2e-07 ***
##     time  error.diff shapes
##  1:  1.5 -0.11039497     24
##  2:  4.5 -0.18406741     24
##  3:  7.5 -0.21299544     24
##  4: 10.5 -0.16726069     24
##  5: 13.5 -0.09783900     24
##  6: 16.5 -0.09179220     24
##  7: 19.5 -0.10061540     24
##  8: 22.5 -0.14712519     24
##  9: 25.5 -0.05935521     16
## 10: 28.5 -0.17055244     24

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTS[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.70375  -0.19717  -0.02273   0.23861   0.72796  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.02461    0.04370  -0.563    0.574
## timeNorm    -0.08556    0.06838  -1.251    0.212
## obj.diff    -0.08977    0.07434  -1.208    0.228
## 
## (Dispersion parameter for gaussian family taken to be 0.09043054)
## 
##     Null deviance: 24.581  on 269  degrees of freedom
## Residual deviance: 24.145  on 267  degrees of freedom
## AIC: 122.35
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5     0.05291005    0.08093653 -0.05322518 27 0.0059 **
##  2:      4.5     0.16931217    0.26803793 -0.14051200 27   0.036 *
##  3:      7.5     0.38095238    0.48102915 -0.09893471 27   0.19 :(
##  4:     10.5     0.45502646    0.59775767 -0.15065745 27   0.052 .
##  5:     13.5     0.64021164    0.69702994 -0.05290538 27   0.32 :(
##  6:     16.5     0.46031746    0.60627919 -0.13542918 27 0.0082 **
##  7:     19.5     0.37037037    0.45492616 -0.09534505 27   0.16 :(
##  8:     22.5     0.40740741    0.54296476 -0.10429981 27   0.014 *
##  9:     25.5     0.37037037    0.51469153 -0.14376601 27   0.046 *
## 10:     28.5     0.35978836    0.54066115 -0.19060030 27   0.028 *
##     time  error.diff shapes
##  1:  1.5 -0.05322518     24
##  2:  4.5 -0.14051200     24
##  3:  7.5 -0.09893471     16
##  4: 10.5 -0.15065745     16
##  5: 13.5 -0.05290538     16
##  6: 16.5 -0.13542918     24
##  7: 19.5 -0.09534505     16
##  8: 22.5 -0.10429981     24
##  9: 25.5 -0.14376601     24
## 10: 28.5 -0.19060030     24

Per group, logical task

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "bad"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.50249  -0.31192   0.00158   0.27300   0.57238  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.18310    0.05115   3.580 0.000366 ***
## timeNorm    -0.01421    0.04068  -0.349 0.726902    
## obj.diff    -0.58809    0.08998  -6.536 1.17e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1006514)
## 
##     Null deviance: 79.537  on 749  degrees of freedom
## Residual deviance: 75.187  on 747  degrees of freedom
## AIC: 411.33
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n        pval
##  1:      1.5      0.3333333     0.4716057 -0.14568003 75 0.00049 ***
##  2:      4.5      0.4057143     0.4928915 -0.09166913 75     0.041 *
##  3:      7.5      0.4361905     0.5070220 -0.06921125 75      0.1 :(
##  4:     10.5      0.3466667     0.4876115 -0.14592638 75 0.00062 ***
##  5:     13.5      0.3885714     0.4705744 -0.08546184 75     0.042 *
##  6:     16.5      0.3809524     0.4634084 -0.08072180 75     0.037 *
##  7:     19.5      0.3523810     0.4739411 -0.13894639 75   0.0027 **
##  8:     22.5      0.3142857     0.4296825 -0.12873142 75   0.0014 **
##  9:     25.5      0.3561905     0.4234421 -0.07931452 75     0.063 .
## 10:     28.5      0.3619048     0.4376259 -0.07920488 75     0.068 .
##     time  error.diff shapes
##  1:  1.5 -0.14568003     24
##  2:  4.5 -0.09166913     24
##  3:  7.5 -0.06921125     16
##  4: 10.5 -0.14592638     24
##  5: 13.5 -0.08546184     24
##  6: 16.5 -0.08072180     24
##  7: 19.5 -0.13894639     24
##  8: 22.5 -0.12873142     24
##  9: 25.5 -0.07931452     16
## 10: 28.5 -0.07920488     16

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "medium"])
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -0.6842  -0.3690   0.0772   0.3315   0.5546  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.22719    0.03565   6.373 3.31e-10 ***
## timeNorm     0.12666    0.04933   2.568   0.0104 *  
## obj.diff    -0.59514    0.07771  -7.659 6.09e-14 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.1277274)
## 
##     Null deviance: 99.075  on 719  degrees of freedom
## Residual deviance: 91.581  on 717  degrees of freedom
## AIC: 566.61
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean  error.diff  n      pval
##  1:      1.5      0.2242063     0.2095827 -0.02888658 72   0.68 :(
##  2:      4.5      0.4305556     0.3149495  0.11381852 72 0.0088 **
##  3:      7.5      0.4166667     0.4085764  0.01184582 72   0.84 :(
##  4:     10.5      0.4880952     0.4423093  0.05303055 72   0.34 :(
##  5:     13.5      0.5059524     0.4224978  0.09425537 72   0.053 .
##  6:     16.5      0.5218254     0.4587626  0.05950609 72   0.14 :(
##  7:     19.5      0.5099206     0.4752146  0.04435498 72   0.43 :(
##  8:     22.5      0.4821429     0.4348125  0.04340068 72   0.23 :(
##  9:     25.5      0.4821429     0.4639661  0.02021695 72   0.66 :(
## 10:     28.5      0.5257937     0.4719986  0.07500839 72   0.15 :(
##     time  error.diff shapes
##  1:  1.5 -0.02888658     16
##  2:  4.5  0.11381852     24
##  3:  7.5  0.01184582     16
##  4: 10.5  0.05303055     16
##  5: 13.5  0.09425537     16
##  6: 16.5  0.05950609     16
##  7: 19.5  0.04435498     16
##  8: 22.5  0.04340068     16
##  9: 25.5  0.02021695     16
## 10: 28.5  0.07500839     16
## Warning: Removed 1 rows containing missing values (geom_errorbar).

## 
## Call:
## glm(formula = error.subj.diff.mise ~ timeNorm + obj.diff, data = DTL[niveau.group == 
##     "good"])
## 
## Deviance Residuals: 
##      Min        1Q    Median        3Q       Max  
## -0.52214  -0.26501  -0.02093   0.25379   0.68368  
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  0.13267    0.02547   5.209 2.46e-07 ***
## timeNorm     0.17621    0.05107   3.451  0.00059 ***
## obj.diff    -0.72141    0.07276  -9.915  < 2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for gaussian family taken to be 0.09487992)
## 
##     Null deviance: 81.624  on 749  degrees of freedom
## Residual deviance: 70.875  on 747  degrees of freedom
## AIC: 367.04
## 
## Number of Fisher Scoring iterations: 2
##     time.bin subj.diff.mean obj.diff.mean   error.diff  n      pval
##  1:      1.5     0.08761905    0.08010825 -0.052761161 75   0.023 *
##  2:      4.5     0.23428571    0.16325372  0.044004349 75   0.22 :(
##  3:      7.5     0.31047619    0.29337315  0.004158512 75   0.93 :(
##  4:     10.5     0.34666667    0.37207770 -0.026222602 75   0.49 :(
##  5:     13.5     0.34476190    0.45469333 -0.114025675 75 0.0029 **
##  6:     16.5     0.36952381    0.50703661 -0.142309890 75 0.0015 **
##  7:     19.5     0.37523810    0.48995306 -0.127835397 75 0.0033 **
##  8:     22.5     0.37142857    0.45150631 -0.089772558 75   0.072 .
##  9:     25.5     0.41904762    0.48919916 -0.072377500 75   0.15 :(
## 10:     28.5     0.44380952    0.52420913 -0.082245022 75   0.15 :(
##     time   error.diff shapes
##  1:  1.5 -0.052761161     24
##  2:  4.5  0.044004349     16
##  3:  7.5  0.004158512     16
##  4: 10.5 -0.026222602     16
##  5: 13.5 -0.114025675     24
##  6: 16.5 -0.142309890     24
##  7: 19.5 -0.127835397     24
##  8: 22.5 -0.089772558     16
##  9: 25.5 -0.072377500     16
## 10: 28.5 -0.082245022     16

Players level analysis

##    play.video.games.group nb
## 1:                      1 60
## 2:                      0 18
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  DT[play.video.games.group == 0]$niveau and DT[play.video.games.group == 1]$niveau
## W = 406, p-value = 0.1134
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.64334978  0.05193345
## sample estimates:
## difference in location 
##             -0.2756005

##    play.video.games.group nb
## 1:                      1 53
## 2:                      0 18
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  DT[play.video.games.group == 0]$niveau and DT[play.video.games.group == 1]$niveau
## W = 409, p-value = 0.3723
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -0.5630621  0.2183961
## sample estimates:
## difference in location 
##             -0.1620475

##    play.video.games.group nb
## 1:                      1 56
## 2:                      0 18
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  DT[play.video.games.group == 0]$niveau and DT[play.video.games.group == 1]$niveau
## W = 374, p-value = 0.1028
## alternative hypothesis: true location shift is not equal to 0
## 95 percent confidence interval:
##  -1.1400431  0.1192438
## sample estimates:
## difference in location 
##             -0.5349009